Detection of Abnormal Regions on Temporal Subtraction Images based on CNN-Reference-Cited by-同舟云学术

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Detection of Abnormal Regions on Temporal Subtraction Images based on CNN

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2019 4th International Conference on Biomedical Signal and Image Processing (ICBIP 2019) - ICBIP '19
language:
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Author:

Nagao Mitsuaki¹,Lu Huimin¹,Kim Hyoungseop¹,Aoki Takatoshi²,Kido Shoji³

Affiliation:

1. Kyushu Institute of Technology, Sensui, Tobata, Kitakyushu, Japan

2. University of Occupational and Environmental Health, Iseigaoka, Yahatanishi, Kitakyusyu, Japan

3. Osaka University Graduate School of Medicine, Yamadaoka, Suita, Osaka, Japan

Publisher

ACM Press

Reference11 articles.

1. International Agency for Research on Cancer: http://globocan.iarc.fr/Pages/fact_sheets_cancer.aspx?cancer=lung (2019/03/13 access).

2. United States Cancer Statics: Data Visualizations: https://gis.cdc.gov/Cancer/USCS/DataViz.html (2019/03/13 access).

3. Non-Small Cell Lung Cancer Survival Rate, by Stage: https://www.cancer.org/cancer/non-small-cell-lung-cancer/detection-diagnosis-staging/survival-rates.html (2019/03/13, access).

4. I.Goodfellow et al., Deep learning, MIT Press, pp. 326--366, 2016.

5. A Krizhenvsky, I Sutskever, G Hinton, ImageNet classification with deep convolutional neural networks, NIPS, pp. 1097--1105, 2012.

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